20 papers · ranked by Valyu relevance
Simone Pernice, Roberta Sirovich, Elena Grassi, Marco Viviani + 10 more
The transition from the evaluation of a single time point to the examination of the entire dynamic evolution of a system is possible only in the presence of the proper framework. The strong variability of dynamic evolution makes the definition of an explanatory procedure for data fitting and data clustering…
Samuel W. Hawes, Andrew K. Littlefield, Daniel A. Lopez, Kenneth J. Sher + 22 more
'Kenneth J. Sher' 'Erin L. Thompson' 'Raul Gonzalez' 'Laika Aguinaldo' 'Ashley R. Adams' 'Mohammadreza Bayat' 'Amy L. Byrd' 'Luis FS Castro-de-Araujo' 'Anthony Dick' 'Steven F. Heeringa' 'Christine M. Kaiver' 'Sarah M. Lehman' 'Lin Li' 'Janosch Linkersdörfer' 'Thomas J. Maullin-Sapey' 'Michael C. Neale' 'Thomas E.…
Pingi, Sharon Torao, Bashar, Md Abul + 2 more
Longitudinal data is commonly utilised across various domains, such as health, biomedical, education and survey studies. This ubiquity has led to a rise in statistical, machine and deep learning-based methods for Longitudinal Data Classification (LDC). However, the intricate nature of the data, characterised by its…
Luoting Zhuang, Stephen H. Park, Steven J. Skates, Ashley E. Prosper + 2 more
Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective monitoring and treatment. However, today’s…
Nicholas Tierney, Dianne Cook, Tania Prvan
Longitudinal (panel) data provide the opportunity to examine temporal patterns of individuals, because measurements are collected on the same person at different, and often irregular, time points. The data is typically visualised using a "spaghetti plot", where a line plot is drawn for each individual. When overlaid in…
Rémi Colin-Chevalier, Frédéric Dutheil, Sébastien Cambier, Samuel Dewavrin + 7 more
'Samuel Dewavrin' 'Thomas Cornet' 'Julien Steven Baker' 'Bruno Pereira' 'Vasile Palade' 'Alireza Daneshkhah' 'Amin Hosseinian-Far' 'Samer A. Kharroubi'] Ever greater technological advances and democratization of digital tools such as computers and smartphones offer researchers new possibilities to collect large amounts…
Suhas V. Vasaikar, Adam K. Savage, Qiuyu Gong, Elliott Swanson + 10 more
Longitudinal bulk and single-cell omics data is increasingly generated for biological and clinical research but is challenging to analyze due to its many intrinsic types of variations. We present PALMO (https://github.com/aifimmunology/PALMO), a platform that contains five analytical modules to examine longitudinal…
Åsa Audulv, Elisabeth O. C. Hall, Åsa Kneck, Thomas Westergren + 5 more
'Liv Fegran' 'Mona Kyndi Pedersen' 'Hanne Aagaard' 'Kristianna Lund Dam' 'Mette Spliid Ludvigsen'] Background Qualitative longitudinal research (QLR) comprises qualitative studies, with repeated data collection, that focus on the temporality (e.g., time and change) of a phenomenon. The use of QLR is increasing in…
Jin Liu
We present the R package nlpsem, which provides a comprehensive set of functions to assess longitudinal processes with individual measurement occasions within the structural equation modeling (SEM) framework. This package focuses on providing computational tools for nonlinear longitudinal models, particularly…
Luoting Zhuang, S. Park, Steven J. Skates, Ashley E. Prosper + 2 more
'Denise R. Aberle' 'William Hsu'] Abstract—Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective…
Nazanin Zounemat-Kermani, Matthew Richardson, Alen Faiz, Siyao Wang + 12 more
Many longitudinal omics studies contain only a small number of repeated measurements collected before, during, or after an intervention. Existing approaches, including mixed-effects models and generalized additive models, estimate temporal effects but do not generally provide a discrete representation of trajectory…
Lin Li, Mohammadreza Bayat, Timothy B. Hayes, Wesley K. Thompson + 2 more
This paper addresses the challenges of managing missing values within expansive longitudinal neu-roimaging datasets, using the specific example of data derived from the Adolescent Brain and Cog-nitive Development (ABCD^®^) study. The conventional listwise deletion method, while widely used, is not recommended due to…
Åsa Audulv, Thomas Westergren, Mette Spliid Ludvigsen, Mona Kyndi Pedersen + 5 more
'Mona Kyndi Pedersen' 'Liv Fegran' 'Elisabeth O. C. Hall' 'Hanne Aagaard' 'Nastasja Robstad' 'Åsa Kneck'] Background Qualitative longitudinal research (QLR) is an emerging methodology used in health research. The method literature states that the change in a phenomenon through time should be the focus of any QLR study…
Marjan Qazvini
England: A 1D-CNN Approach Authors: ['Marjan Qazvini'] Convolutional Neural Networks (CNNs) are proven to be effective when data are homogeneous such as images, or when there is a relationship between consecutive data such as time series data. Although CNNs are not famous for tabular data, we show that we can use them…
Carter J Sevick, Samantha MaWhinney, Peter L Anderson, Camille M Moore
Longitudinal clinical trials and cohort studies often collect clinical data paired with stored biospecimens. An increasing focus of biomedical research is aimed at leveraging these existing specimens to address new research questions. When a hypothesis of interest proposes to utilize costly, limited or difficult to…
Yadugiri V Tiruvaimozhi, Jimmy Borah, Chandra Prakash Kala, Krushnamegh Kunte + 5 more
Long-term ecological monitoring (LTEM) is crucial for understanding ecological processes and responses to environmental change, informing management of natural resources, and biodiversity conservation. Systematic LTEM efforts began in India in the mid-1900s, but there is a lack of comprehensive synthesis of LTEM…
Joseph Davies, David Pattison, Jonathan Hirst
Machine learning models were developed to predict product formation from time-series reaction data for ten Buchwald-Hartwig coupling reactions. The data was provided by DeepMatter and was collected in their DigitalGlassware cloud platform. The reaction probe has 12 sensors to measure properties of interest, including…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
Seyma Alcicek, Piotr Put, Adam Kubrak, Fatih Celal Alcicek + 4 more
NMR relaxometry is an analytical method that provides information about the molecular environment, including even NMR “silent” molecules (spin-0), by analyzing the properties of NMR signals versus the magnitude of the longitudinal field. Conventionally, this technique has been performed at fields much higher than…
Authors not listed
Raman spectroscopy is an increasingly powerful and fast-growing analytical technique across diverse disciplines, from materials science and chemistry to biology and medicine, thanks to advances in Raman instrumentation and greatly supported by the flourishing of chemometrics and artificial intelligence (AI). However…